Collaborative Research: FoMR: Taming the Instruction Bottleneck in Modern Datacenter Applications

合作研究：FoMR：克服现代数据中心应用中的指令瓶颈

• 批准号: 2010810
• 负责人: Baris Kasikci
• 金额: $ 3万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2010810&HistoricalAwards=false
• 关键词: Collaborative; Research; FoMR; Taming; Instruction

Data centers are the power plants that drive the digital economy. These warehouses filled with acres of computers, operated by the likes of Google, Facebook and Amazon, run the websites and apps that we use every day. Yet, as vast as these computing resources are, the amount of code they are tasked with running is larger still and growing at around 20% each year. This project will explore new hardware and software mechanisms to allow large data center programs to better fit within their limited computing resources, to improve data center program performance and energy efficiency, and reduce the toll that power-hungry data centers take on our planet.This project will unfold in three stages. The first stage will leverage new hardware mechanisms for profiling program execution, to obtain an accurate picture of how a data center program is running at the hardware level. The second stage will use that profiling information to adjust the code of the program at runtime to make better use of on-chip caches. The third stage will use the profiling information from the first stage, integrated with new hardware support for code prefetching and code-aware cache replacement, to drive cache utilization even higher than in stage two. Taken together, these optimizations will accelerate data center programs and improve data center energy efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据中心是推动数字经济的发电厂。这些仓库里堆满了数英亩的电脑，由b谷歌、Facebook和亚马逊等公司运营，运行着我们每天使用的网站和应用程序。然而，与这些计算资源一样庞大的是，它们要运行的代码量仍然更大，并且每年以20%左右的速度增长。该项目将探索新的硬件和软件机制，以允许大型数据中心程序更好地适应其有限的计算资源，提高数据中心程序的性能和能源效率，并减少耗电的数据中心对我们星球造成的损失。这个项目将分三个阶段展开。第一阶段将利用新的硬件机制来分析程序执行，以获得数据中心程序如何在硬件级别运行的准确图像。第二阶段将使用该分析信息在运行时调整程序的代码，以便更好地利用片上缓存。第三阶段将使用第一阶段的分析信息，并集成对代码预取和代码感知缓存替换的新硬件支持，从而使缓存利用率比第二阶段更高。总的来说，这些优化将加速数据中心程序并提高数据中心的能源效率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

I-SPY: Context-Driven Conditional Instruction Prefetching with Coalescing

I-SPY：使用合并的上下文驱动条件指令预取

• DOI: 10.1109/micro50266.2020.00024
• 发表时间: 2020
• 期刊: Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO
• 作者: Khan, Tanvir Ahmed;Sriraman, Akshitha;Devietti, Joseph;Pokam, Gilles;Litz, Heiner;Kasikci, Baris
• 通讯作者: Kasikci, Baris

DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling

DMon：使用选择性分析有效检测和纠正数据局部性问题

• 发表时间: 2021
• 期刊: Proceedings of the Symposium on Operating Systems Principles
• 作者: Khan, Tanvir Ahmed;Neal, Ian;Pokam, Gilles;Mozafari, Barzan;Kasikci
• 通讯作者: Kasikci

APT-GET: profile-guided timely software prefetching

• DOI: 10.1145/3492321.3519583
• 发表时间: 2022-03
• 期刊: Proceedings of the Seventeenth European Conference on Computer Systems
• 作者: Saba Jamilan;Tanvir Ahmed Khan;Grant Ayers;Baris Kasikci;Heiner Litz

Thermometer: profile-guided btb replacement for data center applications

• DOI: 10.1145/3470496.3527430
• 发表时间: 2022-06
• 期刊: Proceedings of the 49th Annual International Symposium on Computer Architecture
• 作者: Shixin Song;Tanvir Ahmed Khan;Sara Mahdizadeh-Shahri;Akshitha Sriraman;N. Soundararajan;S. Subramoney;Daniel A. Jiménez;Heiner Litz;Baris Kasikci

OCOLOS: Online COde Layout OptimizationS

OCOLOS：在线代码布局优化

• DOI: 10.1109/micro56248.2022.00045
• 发表时间: 2022
• 期刊: International Symposium on Microarchitecture (MICRO
• 作者: Zhang, Yuxuan;Khan, Tanvir Ahmed;Pokam, Gilles;Kasikci, Baris;Litz, Heiner;Devietti, Joseph
• 通讯作者: Devietti, Joseph